Bayesian inference-based methods are intensive in computation and execution time. Therefore, new techniques are required to analyze the enormous amount of data produced by the operational and upcoming telescopes. ML algorithms are thus designed to extract correct theoretical predictions from training sets (instead of thorough computations of the physical equations at each parameter point). ML methods can efficiently learn from very complex, non-linear, high-order, non-Gaussian priors. The main advantage of using ML is that it trains the program to learn from their experiences and improve themselves. In supervised learning methods, the learning process usually involves optimization of a `cost function' or an `error function' by a technique called back-propagation. Unsupervised learning methods can be used on complex data sets to find different correlations present in the data. There are many other advantages of using unsupervised learning methods as they are capable of self-learning, self-adaption, robustness, and have a dynamically rapid response. Several different machine learning algorithms have been used in various elements of astronomy, astrophysics, statistics and inference, and imaging throughout the last several years. For example, [1]} created an emulator for the 21-cm power spectrum that used artificial neural networks (ANN). This emulator was also utilized within a Bayesian inference framework to restrict the EoR parameters using the 21-cm power spectrum. [2]} used ANN to create an emulator for the 21-cm Global signal, linking astrophysical parameters to the projected global signal. [3]} used Convolutional Neural Networks (CNN) to identify different reionization sources from 21-cm maps. In [4]}, deep learning models were used to simulate the full time developing 21-cm brightness temperature maps from the period of reionization, and the authors compared their anticipated 21-cm maps to the brightness temperature maps obtained by radiative transfer4964.18 simulations. [5]} used CNN to recover the duration of reionization from reionization images, assuming that the midpoint of reionization is already well constrained. [6]} used deep learning and CNN to retrieve astrophysical parameters directly from 21-cm pictures. Machine learning strategies for estimating the 21-cm power spectrum from reionization simulations were compared by [7]}. By training on SKA pictures produced with realistic beam effects, [8]} constructed a convolutional de-noising autoencoder (CDAE) to recover the EoR signal. [9]} applied CNN to 3D-tomographic 21-cm images for parameter estimation and posterior inference.
